GIS and Machine Learning for Small Area Classifications in Developing Countries - Ojo, Adegbola
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Présentation Gis And Machine Learning For Small Area Classifications In Developing Countries Format Relié
- Livre Sciences de la vie et de la terre
Résumé :
Using two developing countries as case studies for small area segmentation techniques, the book connects new trans-disciplinary ways of thinking about social and spatial inequalities from a scientific perspective with GIS. This offers stakeholders a framework for engaging in practical dialogue on development policy within urban and rural settings.
Biographie: Dr. Adegbola Ojo is Director of Teaching and Learning, Programme Leader, and Senior Lecturer in Urban Geography and Applications of Big Data at the School of Geography, University of Lincoln, UK. He received his PhD in Quantitative Human Geography from the University of Sheffield, his MSc in Geographic Information Science from the University College London, and his BSc in Geography and Planning Sciences from the University of Ado-Ekiti, Nigeria. His research interests are focused in understanding and representing social and spatial dynamics and intricacies of population behavior within a framework of Interdisciplinary Studies, Population Geography, Quantitative Social Science, and Computer Modeling. His research activities are grouped around the development and application of small area classifications, geographic information systems and geographic information science for informing public policy. Dr. Ojo has published many monographs and research articles with reputable journals. He has designed and delivered lectures, workshops, seminars, tutorials, practical labs, and assessments to a range of undergraduate and graduate students and working professionals.
Sommaire: PART 1: BACKGROUND, CONCEPTS AND DEFINITIONS 1. Introduction 2. Origins and Concept of Social Area Classification 3. Public Policy Prospects of Small Area Classifications for Developing Countries 4. Reasons for Slow Proliferation of Area Classifications across Developing Countries PART 2: UNDERLYING TECHNIQUES AND DEPLOYMENT APPROACHES 5. Building Blocks: Spatial Data Preparation 6. Machine Learning Methods for Building Small Area Classifications 7. Visualizing Small Area Geodemographics Data and Information Products PART 3: ILLUSTRATIVE APPLICATIONS AND CONCLUSION 8. The Grouping of Nigerian Local Government Areas 9. Combining Continuous and Categorical Data to Segment Philippines Barangays 10. Modeling Temporal Distribution and Seasonality of Infectious Diseases with Area Classifications 11. Segmenting Gender Gaps in Levels of Educational Attainment 12. Conclusion